Proc. of the International Symposium on Nonlinear Theory and its Applications (NOLTA), pp. 411-414, Xi’an, 2002
Prediction of the next stock price using neural network for data mining
H. Takaho, T. Arai, T. Otake and M. Tanaka
Abstract: Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties for data mining, knowledge discovery and prediction of sample points. In this paper, we introduce the method for the prediction of the next sample point with milti-layer neural network  in TSDB. Predicting the next sample point in TSDB includes cleaning and filtering the time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques, and TSDB for the closing price of a stock are used as an example.